| Empirical evaluation of rectified activations in convolutional network B Xu, N Wang, T Chen, M Li arXiv preprint arXiv:1505.00853, 2015 | 1913 | 2015 |
| Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems T Chen, M Li, Y Li, M Lin, N Wang, M Wang, T Xiao, B Xu, C Zhang, ... arXiv preprint arXiv:1512.01274, 2015 | 1856 | 2015 |
| Scaling distributed machine learning with the parameter server M Li, DG Andersen, JW Park, AJ Smola, A Ahmed, V Josifovski, J Long, ... 11th {USENIX} Symposium on Operating Systems Design and Implementation …, 2014 | 1333 | 2014 |
| Efficient mini-batch training for stochastic optimization M Li, T Zhang, Y Chen, AJ Smola Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014 | 592 | 2014 |
| Bag of tricks for image classification with convolutional neural networks T He, Z Zhang, H Zhang, Z Zhang, J Xie, M Li Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 456 | 2019 |
| Communication efficient distributed machine learning with the parameter server M Li, DG Andersen, AJ Smola, K Yu Advances in Neural Information Processing Systems 27, 19-27, 2014 | 434 | 2014 |
| Emotion classification based on gamma-band EEG M Li, BL Lu 2009 Annual International Conference of the IEEE Engineering in medicine and …, 2009 | 363 | 2009 |
| Resnest: Split-attention networks H Zhang, C Wu, Z Zhang, Y Zhu, H Lin, Z Zhang, Y Sun, T He, J Mueller, ... arXiv preprint arXiv:2004.08955, 2020 | 261 | 2020 |
| Dive into deep learning A Zhang, ZC Lipton, M Li, AJ Smola arXiv preprint arXiv:2106.11342, 2021 | 223* | 2021 |
| Parameter Server for Distributed Machine Learning M Li, L Zhou, Z Yang, A Li, F Xia, DG Andersen, A Smola | 159 | 2013 |
| Making large-scale Nyström approximation possible M Li, JTY Kwok, B Lü ICML 2010-Proceedings, 27th International Conference on Machine Learning, 631, 2010 | 133 | 2010 |
| Large-scale Nyström kernel matrix approximation using randomized SVD M Li, W Bi, JT Kwok, BL Lu IEEE transactions on neural networks and learning systems 26 (1), 152-164, 2014 | 96 | 2014 |
| Iterative row sampling M Li, GL Miller, R Peng 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, 127-136, 2013 | 84 | 2013 |
| GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing. J Guo, H He, T He, L Lausen, M Li, H Lin, X Shi, C Wang, J Xie, S Zha, ... J. Mach. Learn. Res. 21 (23), 1-7, 2020 | 81 | 2020 |
| Bag of freebies for training object detection neural networks Z Zhang, T He, H Zhang, Z Zhang, J Xie, M Li arXiv preprint arXiv:1902.04103, 2019 | 77 | 2019 |
| Time and space efficient spectral clustering via column sampling M Li, XC Lian, JT Kwok, BL Lu CVPR 2011, 2297-2304, 2011 | 67 | 2011 |
| Optimizing {CNN} model inference on cpus Y Liu, Y Wang, R Yu, M Li, V Sharma, Y Wang 2019 {USENIX} Annual Technical Conference ({USENIX}{ATC} 19), 1025-1040, 2019 | 63 | 2019 |
| Revise saturated activation functions B Xu, R Huang, M Li arXiv preprint arXiv:1602.05980, 2016 | 56 | 2016 |
| Difacto: Distributed factorization machines M Li, Z Liu, AJ Smola, YX Wang Proceedings of the Ninth ACM International Conference on Web Search and Data …, 2016 | 56 | 2016 |
| Distributed delayed proximal gradient methods M Li, DG Andersen, A Smola NIPS Workshop on Optimization for Machine Learning 3, 3, 2013 | 55 | 2013 |
